Enhancing quotation accuracy assessment with Chatpdf – a game-changer for a century-old conundrum

Authors

  • Muhammad Talal Ibrahim Section of Orthopedics, Department of Surgery, Aga Khan University Hospital,
  • Cole Vincent Veliky Ohio State University College of Medicine, Columbus, Ohio, USA
  • Syeda Sadia Fatima Department of Biological and Biomedical Sciences, Aga Khan University,
  • Zahra Hoodbhoy Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
  • Shahryar Noordin Section of Orthopedics, Department of Surgery, Aga Khan University Hospital,

DOI:

https://doi.org/10.47391/JPMA.AKU-10Surg-28

Abstract

Quotation errors compromise the reliability of published medical literature, but assessing these errors is very timeintensive. The current observational cross-sectional study was planned to assess the accuracy and speed of detecting quotation errors using the ChatPDF programme that is powered by artificial intelligence. Of the 398 quotations assessed, 310(77.9%) were fully supported, while 88(22.1%) had errors. Among the former quotations, the ChatPDF programme could comprehensively highlight 210(67.7%) PDFs, provided comprehensive answers in 262(84.5%), and was completely helpful in 248(80%) cases. In contrast, for erroneous quotations, the corresponding values were 36(40.9%), 54(61.4%) and 43(48.9%), respectively (p<0.001). ChatPDF was found to have immense potential to revolutionise quotation error assessments.

Keyword: Artificial intelligence, Natural language processing, ChatPDF, Referencing error, Quotation error.

Published

2026-05-18

How to Cite

Muhammad Talal Ibrahim, Cole Vincent Veliky, Syeda Sadia Fatima, Zahra Hoodbhoy, & Shahryar Noordin. (2026). Enhancing quotation accuracy assessment with Chatpdf – a game-changer for a century-old conundrum. Journal of the Pakistan Medical Association, 76(05 (Supp-1), S150–S. https://doi.org/10.47391/JPMA.AKU-10Surg-28